Evaluation of pediatric CNS malignancies with (99m)Tc-methoxyisobutylisonitrile SPECT.
Bibliographic record
Abstract
UNLABELLED: SPECT has the potential to add valuable information to the diagnosis and management of central nervous system (CNS) malignancy. Radioactive tracers including (99m)Tc-methoxyisobutylisonitrile (MIBI), or sestamibi, have been shown to be sensitive markers for brain tumors; however, their role in imaging children is poorly defined. METHODS: We undertook a pilot study of 29 pairs of (99m)Tc-MIBI and MRI images from 20 children to explore the clinical usefulness of this tracer in CNS malignancy. RESULTS: Tumor types that took up (99m)Tc-MIBI included brain stem glioma, fibrillary astrocytoma, other low-grade astrocytomas, and glioblastoma multiforme. Most tumors positive for (99m)Tc-MIBI uptake were astrocytomas, including those in the brain stem, cerebellum, and cortex. This method of nuclear imaging not only was able to identify the presence of a tumor but also could identify changes in the same tumor over time. Some correlation between histologic grade and (99m)Tc-MIBI uptake was observed. Several tumors, including craniopharyngioma, medulloblastoma, and optic glioma, were evident on MRI but not on (99m)Tc-MIBI SPECT. CONCLUSION: The results suggest that this modality is a potentially useful tool in the diagnosis and management of CNS malignancies, particularly higher-grade astrocytomas, in children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".